---
title: 'Persistent Workers'
---



This page covers how to use persistent workers, the benefits, requirements, and
how workers affect sandboxing.

A persistent worker is a long-running process started by the Bazel server, which
functions as a *wrapper* around the actual *tool* (typically a compiler), or is
the *tool* itself. In order to benefit from persistent workers, the tool must
support doing a sequence of compilations, and the wrapper needs to translate
between the tool's API and the request/response format described below. The same
worker might be called with and without the `--persistent_worker` flag in the
same build, and is responsible for appropriately starting and talking to the
tool, as well as shutting down workers on exit. Each worker instance is assigned
(but not chrooted to) a separate working directory under
`<outputBase>/bazel-workers`.

Using persistent workers is an
[execution strategy](/docs/user-manual#execution-strategy) that decreases
start-up overhead, allows more JIT compilation, and enables caching of for
example the abstract syntax trees in the action execution. This strategy
achieves these improvements by sending multiple requests to a long-running
process.

Persistent workers are implemented for multiple languages, including Java,
[Scala](https://github.com/bazelbuild/rules_scala),
[Kotlin](https://github.com/bazelbuild/rules_kotlin), and more.

Programs using a NodeJS runtime can use the
[@bazel/worker](https://www.npmjs.com/package/@bazel/worker) helper library to
implement the worker protocol.

## Using persistent workers

[Bazel 0.27 and higher](https://blog.bazel.build/2019/06/19/list-strategy.html)
uses persistent workers by default when executing builds, though remote
execution takes precedence. For actions that do not support persistent workers,
Bazel falls back to starting a tool instance for each action. You can explicitly
set your build to use persistent workers by setting the `worker`
[strategy](/docs/user-manual#execution-strategy) for the applicable tool
mnemonics. As a best practice, this example includes specifying `local` as a
fallback to the `worker` strategy:

```posix-terminal
bazel build //{{ '<var>' }}my:target{{ '</var>' }} --strategy=Javac=worker,local
```

Using the workers strategy instead of the local strategy can boost compilation
speed significantly, depending on implementation. For Java, builds can be 2–4
times faster, sometimes more for incremental compilation. Compiling Bazel is
about 2.5 times as fast with workers. For more details, see the
"[Choosing number of workers](#number-of-workers)" section.

If you also have a remote build environment that matches your local build
environment, you can use the experimental
[*dynamic* strategy](https://blog.bazel.build/2019/02/01/dynamic-spawn-scheduler.html),
which races a remote execution and a worker execution. To enable the dynamic
strategy, pass the
[--experimental_spawn_scheduler](/reference/command-line-reference#flag--experimental_spawn_scheduler)
flag. This strategy automatically enables workers, so there is no need to
specify the `worker` strategy, but you can still use `local` or `sandboxed` as
fallbacks.

## Choosing number of workers

The default number of worker instances per mnemonic is 4, but can be adjusted
with the
[`worker_max_instances`](/reference/command-line-reference#flag--worker_max_instances)
flag. There is a trade-off between making good use of the available CPUs and the
amount of JIT compilation and cache hits you get. With more workers, more
targets will pay start-up costs of running non-JITted code and hitting cold
caches. If you have a small number of targets to build, a single worker may give
the best trade-off between compilation speed and resource usage (for example,
see [issue #8586](https://github.com/bazelbuild/bazel/issues/8586).
The `worker_max_instances` flag sets the maximum number of worker instances per
mnemonic and flag set (see below), so in a mixed system you could end up using
quite a lot of memory if you keep the default value. For incremental builds the
benefit of multiple worker instances is even smaller.

This graph shows the from-scratch compilation times for Bazel (target
`//src:bazel`) on a 6-core hyper-threaded Intel Xeon 3.5 GHz Linux workstation
with 64 GB of RAM. For each worker configuration, five clean builds are run and
the average of the last four are taken.

![Graph of performance improvements of clean builds](/docs/images/workers-clean-chart.png "Performance improvements of clean builds")

**Figure 1.** Graph of performance improvements of clean builds.

For this configuration, two workers give the fastest compile, though at only 14%
improvement compared to one worker. One worker is a good option if you want to
use less memory.

Incremental compilation typically benefits even more. Clean builds are
relatively rare, but changing a single file between compiles is common, in
particular in test-driven development. The above example also has some non-Java
packaging actions to it that can overshadow the incremental compile time.

Recompiling the Java sources only
(`//src/main/java/com/google/devtools/build/lib/bazel:BazelServer_deploy.jar`)
after changing an internal string constant in
[AbstractContainerizingSandboxedSpawn.java](https://github.com/bazelbuild/bazel/blob/master/src/main/java/com/google/devtools/build/lib/sandbox/AbstractContainerizingSandboxedSpawn.java)
gives a 3x speed-up (average of 20 incremental builds with one warmup build
discarded):

![Graph of performance improvements of incremental builds](/docs/images/workers-incremental-chart.png "Performance improvements of incremental builds")

**Figure 2.** Graph of performance improvements of incremental builds.

The speed-up depends on the change being made. A speed-up of a factor 6 is
measured in the above situation when a commonly used constant is changed.

## Modifying persistent workers

You can pass the
[`--worker_extra_flag`](/reference/command-line-reference#flag--worker_extra_flag)
flag to specify start-up flags to workers, keyed by mnemonic. For instance,
passing `--worker_extra_flag=javac=--debug` turns on debugging for Javac only.
Only one worker flag can be set per use of this flag, and only for one mnemonic.
Workers are not just created separately for each mnemonic, but also for
variations in their start-up flags. Each combination of mnemonic and start-up
flags is combined into a `WorkerKey`, and for each `WorkerKey` up to
`worker_max_instances` workers may be created. See the next section for how the
action configuration can also specify set-up flags.

Passing the
[`--worker_sandboxing`](/reference/command-line-reference#flag--worker_sandboxing)
flag makes each worker request use a separate sandbox directory for all its
inputs. Setting up the [sandbox](/docs/sandboxing) takes some extra time,
especially on macOS, but gives a better correctness guarantee.

The
[`--worker_quit_after_build`](/reference/command-line-reference#flag--worker_quit_after_build)
flag is mainly useful for debugging and profiling. This flag forces all workers
to quit once a build is done. You can also pass
[`--worker_verbose`](/reference/command-line-reference#flag--worker_verbose) to
get more output about what the workers are doing. This flag is reflected in the
`verbosity` field in `WorkRequest`, allowing worker implementations to also be
more verbose.

Workers store their logs in the `<outputBase>/bazel-workers` directory, for
example
`/tmp/_bazel_larsrc/191013354bebe14fdddae77f2679c3ef/bazel-workers/worker-1-Javac.log`.
The file name includes the worker id and the mnemonic. Since there can be more
than one `WorkerKey` per mnemonic, you may see more than `worker_max_instances`
log files for a given mnemonic.

For Android builds, see details at the
[Android Build Performance page](/docs/android-build-performance).

## Implementing persistent workers

See the [creating persistent workers](/remote/creating) page for more
information on how to make a worker.

This example shows a Starlark configuration for a worker that uses JSON:

```python
args_file = ctx.actions.declare_file(ctx.label.name + "_args_file")
ctx.actions.write(
    output = args_file,
    content = "\n".join(["-g", "-source", "1.5"] + ctx.files.srcs),
)
ctx.actions.run(
    mnemonic = "SomeCompiler",
    executable = "bin/some_compiler_wrapper",
    inputs = inputs,
    outputs = outputs,
    arguments = [ "-max_mem=4G",  "@%s" % args_file.path],
    execution_requirements = {
        "supports-workers" : "1", "requires-worker-protocol" : "json" }
)
```

With this definition, the first use of this action would start with executing
the command line `/bin/some_compiler -max_mem=4G --persistent_worker`. A request
to compile `Foo.java` would then look like:

NOTE: While the protocol buffer specification uses "snake case" (`request_id`),
the JSON protocol uses "camel case" (`requestId`). In this document, we will use
camel case in the JSON examples, but snake case when talking about the field
regardless of protocol.

```json
{
  "arguments": [ "-g", "-source", "1.5", "Foo.java" ]
  "inputs": [
    { "path": "symlinkfarm/input1", "digest": "d49a..." },
    { "path": "symlinkfarm/input2", "digest": "093d..." },
  ],
}
```

The worker receives this on `stdin` in newline-delimited JSON format (because
`requires-worker-protocol` is set to JSON). The worker then performs the action,
and sends a JSON-formatted `WorkResponse` to Bazel on its stdout. Bazel then
parses this response and manually converts it to a `WorkResponse` proto. To
communicate with the associated worker using binary-encoded protobuf instead of
JSON, `requires-worker-protocol` would be set to `proto`, like this:

```
  execution_requirements = {
    "supports-workers" : "1" ,
    "requires-worker-protocol" : "proto"
  }
```

If you do not include `requires-worker-protocol` in the execution requirements,
Bazel will default the worker communication to use protobuf.

Bazel derives the `WorkerKey` from the mnemonic and the shared flags, so if this
configuration allowed changing the `max_mem` parameter, a separate worker would
be spawned for each value used. This can lead to excessive memory consumption if
too many variations are used.

Each worker can currently only process one request at a time. The experimental
[multiplex workers](/remote/multiplex) feature allows using multiple
threads, if the underlying tool is multithreaded and the wrapper is set up to
understand this.

In
[this GitHub repo](https://github.com/Ubehebe/bazel-worker-examples),
you can see example worker wrappers written in Java as well as in Python. If you
are working in JavaScript or TypeScript, the
[@bazel/worker package](https://www.npmjs.com/package/@bazel/worker)
and
[nodejs worker example](https://github.com/bazelbuild/rules_nodejs/tree/stable/examples/worker)
might be helpful.

## How do workers affect sandboxing?

Using the `worker` strategy by default does not run the action in a
[sandbox](/docs/sandboxing), similar to the `local` strategy. You can set the
`--worker_sandboxing` flag to run all workers inside sandboxes, making sure each
execution of the tool only sees the input files it's supposed to have. The tool
may still leak information between requests internally, for instance through a
cache. Using `dynamic` strategy
[requires workers to be sandboxed](https://github.com/bazelbuild/bazel/blob/master/src/main/java/com/google/devtools/build/lib/exec/SpawnStrategyRegistry.java).

To allow correct use of compiler caches with workers, a digest is passed along
with each input file. Thus the compiler or the wrapper can check if the input is
still valid without having to read the file.

Even when using the input digests to guard against unwanted caching, sandboxed
workers offer less strict sandboxing than a pure sandbox, because the tool may
keep other internal state that has been affected by previous requests.

Multiplex workers can only be sandboxed if the worker implementation support it,
and this sandboxing must be separately enabled with the
`--experimental_worker_multiplex_sandboxing` flag. See more details in
[the design doc](https://docs.google.com/document/d/1ncLW0hz6uDhNvci1dpzfEoifwTiNTqiBEm1vi-bIIRM/edit)).

## Further reading

For more information on persistent workers, see:

*   [Original persistent workers blog post](https://blog.bazel.build/2015/12/10/java-workers.html)
*   [Haskell implementation description](https://www.tweag.io/blog/2019-09-25-bazel-ghc-persistent-worker-internship/)
*   [Blog post by Mike Morearty](https://medium.com/@mmorearty/how-to-create-a-persistent-worker-for-bazel-7738bba2cabb)
*   [Front End Development with Bazel: Angular/TypeScript and Persistent Workers
    w/ Asana](https://www.youtube.com/watch?v=0pgERydGyqo)
*   [Bazel strategies explained](https://jmmv.dev/2019/12/bazel-strategies.html)
*   [Informative worker strategy discussion on the bazel-discuss mailing list](https://groups.google.com/forum/#!msg/bazel-discuss/oAEnuhYOPm8/ol7hf4KWJgAJ)
